Support vector machine regression for project control forecasting
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Publication type
Journal article with impact factorPublication Year
2014Journal
Automation in ConstructionPublication Volume
47Publication Issue
NovemberPublication Begin page
92Publication End page
106
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Support Vector Machines are methods that stem from Artificial Intelligence and attempt to learn the relation between data inputs and one or multiple output values. However, the application of these methods has barely been explored in a project control context. In this paper, a forecasting analysis is presented that compares the proposed Support Vector Regression model with the best performing Earned Value and Earned Schedule methods. The parameters of the SVM are tuned using a cross-validation and grid search procedure, after which a large computational experiment is conducted. The results show that the Support Vector Machine Regression outperforms the currently available forecasting methods. Additionally, a robustness experiment has been set up to investigate the performance of the proposed method when the discrepancy between training and test set becomes larger.Keyword
Operations & Supply Chain Management, Earned Value Management (EVM), Support Vector Regression (SVR), PredictionKnowledge Domain/Industry
Operations & Supply Chain Managementae974a485f413a2113503eed53cd6c53
10.1016/j.autcon.2014.07.014